Current algorithmic approaches for the early identification of sepsis rely on processing data streams with a high level of complexity and granularity. For example, one prior art method derives the instantaneous heart rate from electrocardiogram (ECG) waveform data acquired at 1000 readings a second. The prior art data processing algorithms are inherently complex, often appearing as a black box to end-users. An increasing number of studies indicate that properly designed and effectively used Clinical Decision Support Systems (CDSSs) have the ability to improve quality of patient care. As an example, such a study is described in: Wright A, Sittig D F, Ash J S, Sharma S, Pang J E, Middleton B. Clinical decision support capabilities of commercially-available clinical information systems. J Am Med Inform Assn. 2009; 16(5):637-44.
The black box approach of the prior art raises concerns about the possible negative effects of CDSSs, including: potential de-skilling effects if system users do not understand how results were generated; a lack of flexibility and overly prescriptive outcomes; promoting over-reliance on software applications, which is a risk in the event of system failure when systems provide risk indexes and clinicians do not know how they were derived; and difficulty in evaluating outcomes. Such possible negative effects are described in: Open Clinical. Potential benefits and drawbacks of the use of CDSSs; Factors which may help determine the successful use of CDSSs in clinical practice [Internet]. 2005. Available from: http://www.openclinical.org/dssSuccessFactors.html.
Additionally, in many prior art Neonatal Intensive Care Units (NICUs) it is not possible to acquire and store data at a high enough sampling frequency to support the Heart Rate Variability (HRV) algorithms. These limitations may explain the small number of research level HRV analysis systems that translate from ‘bench to bedside’ and challenges associated with enabling real-time support in clinical practice.
HRV is the oscillation in the interval between consecutive heart beats, as is described in Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart rate variability standards of measurement, physiological interpretation and clinical use. Circulation. 1996; 93:1043-1065. The potential medical application of monitoring HRV in newborn infants arises from the observation that abnormal HRV is associated with neonatal morbidity and mortality; therefore, HRV may have the potential to provide a non-invasive diagnostic tool for clinically important conditions of the newborn infant. This possibility is discussed in: de Beer N A M, Andriessen P, Berendsen R C M, Oei S G, Wijn P F F, Bambang Oetomo S B. Customized spectral band analysis compared with conventional Fourier analysis of heart rate variability in neonates. Physiol Meas. 2004; 25(6):1385-1395.
Reduced HRV in neonates has been associated with respiratory distress syndrome (as discussed in Aarimaa T, Oja R, Antila K, Valimaki 1. Interaction of heart rate and respiration in newborn babies. Pediatr Res. 1988; 24(6):745-750), birth asphyxia and intraventricular hemorrhage (as discussed in, Divon M Y, Winkler H, Yeh S Y, Platt L D, Lamger O, Merkatz I R. Diminished respiratory sinus arrhythmia in asphyxiated term infants. Am J Obstet Gynecol. 1986; 155(6):1263-6 and Prietsch V, Knoepkeb U, Obladenc M. Continuous monitoring of heart rate variability in preterm infants. Early Hum Dev. 1994; 37(2):117-131). In addition, substantial research has shown that abnormal heart rate characteristics precede the subtle clinical features of Late Onset Neonatal Sepsis (LONS). Previous prior art work on early detection of sepsis has described mathematical processing of the instantaneous Heart Rate (HR) to obtain characteristics such as HRV (as discussed in Griffin M P, Lake D E, Moorman J R. Heart rate characteristics and laboratory tests in neonatal sepsis Pediatrics. 2005; 115(4):937-41; Griffin M P, O'Shea T M, Bissonette E A, Harrell F E, Lake D E, Moormman J R. Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness. Pediatr Res. 2003; 53(6):920-6; and Griffin M P, Moornian R. Using novel heart rate analysis. Pediatrics. 2001; 107(1):97-104) and HR decelerations (as discussed in Flower A A, Moorman J R, Lake D E, Delos J B. Periodic heart rate decelerations in premature infants. Experimental Biology and Medicine. 2010; 235(4):531-8).
Newborn infants, especially premature infants, are very susceptible to infectious pathogens (as discussed in (Ganatra H a, Stoll B J, Zaidi A K M. International perspective on early-onset neonatal sepsis. Clin Perinatol. 2010:37(2):501-523). Early diagnosis of sepsis can be important because infants are often diagnosed only when seriously ill which decreases the probability for prompt, complete recovery with antibiotic therapy. Diagnosing neonatal sepsis is a challenging problem because it does not conform to a ‘typical’ presentation (as is discussed in Gwadry-Sridhar F, Lewden B, Mequanint 5, Bauer M_Comparison of analytic approaches for determining variables—a case study in predicting the likelihood of sepsis. Proceedings of HEALTHINF; 2009: Porto, Portugal: 90-96) and the signs of sepsis in the neonate are often nonspecific (as is discussed in Griffin M P, Lake D E, M O T, Moorman J R. Heart rate characteristics and clinical signs in neonatal sepsis. Pediatr Res. 2007; 61(2):222-227, and Beck-Sague C M, Azimi P, Fonseca S N, Baltimore R S, Powell D A, Bland L A, et al. Bloodstream infections in neonatal intensive care unit patients: results of a multicenter study. Pediatr Infect Dis J. 1994; 13(12):1110-1116).
There are two sepsis classifications; early-onset neonatal sepsis (EONS) and LONS, where EONS is typically defined as sepsis occurring within the first three or 7 days after birth and LONS occurring as early as four days after birth and as late as 28 days after birth; for the purpose of this study we use the definition that EONS is sepsis acquired in the first 4 days of life and LONS refers to sepsis acquired on or after the fifth day of life. Studies have shown that LONS occurs in approximately 10% of all neonates and in more than 25% of very low birth weight infants who are hospitalized in NICUs.
In 2001, Griffin and Moorman published novel results based on monitoring neonates with risk factors for acquiring LONS. They concluded that patients that developed sepsis and sepsis-like illness had reduced HRV and short HR decelerations for up to 24 hours preceding clinical deterioration (as discussed in Griffin M P, Moornian R. Using novel heart rate analysis. Pediatrics. 2001; 107(1):97-104). Further prior art studies found that these heart rate characteristics (HRC) added significantly to the predictive information of birth weight, gestational age, and days of age. Further refinement to these studies added an illness severity score; combined this score with HRCs; and used multivariate logistic regression to create a risk assessment card for sepsis. A combined model based on their logistic based approach and k-nearest neighbour analysis yielded a receiver operator characteristic of 0.87 (as discussed in Xiao Y, Griffin M P, Lake D E, Moorman J R. Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis. Med Decis Making. 2010; 30(2):25866). These findings indicate that subtle changes, which may not be apparent through manual recordings at regular intervals, can be important in detecting the onset of sepsis in neonates. However, in the majority of NICUs, current information management practices do not make provision for storage and analysis of real-time data streams, with manual recordings every 30-60 minutes being the norm.
Although infants in the NICU frequently receive medications that affect the nervous system, relatively little has been published on the impact of these drugs on neonatal HRV and possible associated limitations in using HRV as an early indicator of LONS. In de Beer N A M, Andriessen P, Berendsen R C M, Oei S G, Wijn P F F, Bambang Oetomo S B. Customized spectral band analysis compared with conventional Fourier analysis of heart rate variability in neonates. Physiol Meas. 2004; 25(6):1385-1395, the authors demonstrate that atropine, which is a muscarinic receptor antagonist that is used to inhibit the effects of excessive vagal activation on the heart, resulting in large variations in HRV before and after atropine. While it is known that HRV occurs at the same frequency as respiration and is under the control of the parasympathetic branch of the autonomic nervous system (as is discussed in Brown L. Heart rate variability in premature infants during feeding. Biological Research for Nursing. 2007; 8(4):283-93), the relationship between HRV and RRV, in the presence of confounders, such as narcotics and other drugs, is not clear.
Prior art methods focus upon the relationship between HRV and specific clinical conditions, For example, Loforte et al studied HRV and the association between the heart's RR-wave intervals and the spontaneous respiration in a selected population of sick premature infants and found that lower relationship values were strongly associated with sepsis (Loforte R, Carrault G, Mainardi L, Beuche A. Heart rate and respiration relationships as a diagnostic tool for late onset sepsis in sick preterm infants. Computers in Cardiology. 2006; 33:737-740). The Loforte paper hypothesizes that exploration of HRV and respiration relationships may provide an indicator of infection in premature newborns.
As another example, Saria et al. developed an individualized risk scoring system for preterm, low birth weight infants (≦34 weeks gestation, birth weight ≦2000 g) using three non-invasive physiological parameters-heart rate, respiratory rate, and oxygen saturation-acquired during the first three hours of life together with gestational age and birth weight to predict morbidity, including infection (Saria S, Rajani A K, Gould J, Koller D, Penn A A. Integration of early physiological responses predicts later illness severity in preterm infants. Sci Transl Med. 2010; 2(48):1-8). The Saria paper makes a comparison with an electronic Apgar score, as it is predictive of future illness severity. This work calculated HRV and RRV using mean values plus baseline and residual variability signals. However, the authors stated that they selected the first three hours of life because this time period was less likely to be confounded by medical interventions, such as surgery, narcotics or other drugs.
Additional relevant prior art includes: Adlassnig K P, Combi C, Das A K, Keravnou E T, Pozzi G. Temporal representation and reasoning in medicine: Research directions and challenges. Artif Intell Med. 2006; 38(2):101-13; Post A R, Harrison J H. Temporal data mining. Clin Lab Med. 2008; 28(1):83-100; McGregor C. System, method and computer program for multi-dimensional temporal data mining. 2010. Patent 4 089705-0009; Canada, Gatineau Quebec; and McGregor C, Sow D, James A, Blount M, Ebling M, Eklund J, et al. Collaborative research on an intensive care decision support system utilizing physiological data streams. AMIA Annu Symp Proc; 2009:1124-6.
As is shown in the prior art, to date known methods that analyze data in this field of art have been highly based in statistics. Therefore, analyzing cross correlations of temporal behaviours is too computationally complex to be incorporated with prior art methods. Additionally, there has also been extensive focus on just the heart rate behaviour to the exclusion of other factors. For example, prior art real-time monitoring of physiological data shows a focus on the detection of the heart beat (known as the QRS complex) within the electrocardiogram (ECG) and analyzing the distance between two same parts (R) of the beating heart process. That distance is known as the R-R interval. The behaviour that may be determined from such methods is a reduced variance over time in the distance from one beat to the next over a sequential collection of R-R intervals.
Moreover, prior art methods function to utilize offline data that has been collected previously by way of computationally intensive techniques such as sample entropy, frequency histograms for a given time interval or standard deviations. Applying such maths to assess the state of variability is overly complex and is often not translatable to a computational method that can be run in real-time. For example, known sample entropy methods rely on looking at a significant number of intervals from the immediate past, and then using that to try and probabilistically see if it can guess the values in the future and the more accurate that ability to predict then the higher the score. This method therefore requires the availability of information from the future to see if the prediction was correct. Such a method cannot be run in real-time to provide useful results, as it requires operation of delay of several minutes. The result is that what just happened in the immediate past is essentially recognized as the future in such methods.
Moreover, standard deviation of the values of HR does not provide information regarding the distances from one HR to the next, but rather provides information about the spread of the values overall. The results do not provide details of variability because the HRs occur as a sequential stream that sequential information is integrated into the analysis.